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There has been considerable interest in the development of innovative service delivery modules for prioritizing resources in sexual health delivery in response to dwindling fiscal resources and rising infection rates.This study aims to derive and validate a risk scoring algorithm to accurately identify asymptomatic patients at increased risk for chlamydia and/or gonorrhea infection. We examined the electronic records of patient visits at sexual health clinics in Vancouver, Canada. We derived risk scores from regression coefficients of multivariable logistic regression model using visits between 2000 and 2006. We evaluated the model’s discrimination, calibration, and screening performance. Temporal validation was assessed in visits from 2007 to 2012.The prevalence of infection was 1.8% (n = 10,437) and 2.1% (n = 14,956) in the derivation and validation data sets, respectively. The final model included younger age, nonwhite ethnicity, multiple sexual partners, and previous infection and showed reasonable performance in the derivation (area under the receiver operating characteristic curve = 0.74; Hosmer-Lemeshow P = 0.91) and validation (area under the receiver operating characteristic curve = 0.64; Hosmer-Lemeshow P = 0.36) data sets. A risk score cutoff point of at least 6 detected 91% and 83% of cases by screening 68% and 68% of the derivation and validation populations, respectively.These findings support the use of the algorithm for individualized risk assessment and have important implications for reducing unnecessary screening and saving costs. Specifically, we anticipate that the algorithm has potential uses in alternative settings such as Internet-based testing contexts by facilitating personalized test recommendations, stimulating health care–seeking behavior, and aiding risk communication by increasing sexually transmitted infection risk perception through the creation of tailored risk messages to different groups.